WM-VLM: world model generates visual intermediate states for spatial reasoning
ZhitingHu · x · 2026-10-06
Researchers introduce WM-VLM, a paradigm where an internal world model generates intermediate visual states that a VLM uses to solve spatial reasoning tasks.
- Significantly outperforms the SFTed VLM backbone on tasks requiring 'imagination'
- Uses a light MoT architecture with an understanding branch (VLM) and a generation branch (WM), efficient for both visual thoughts and standard verbal reasoning
- Authors argue interleaved visual-textual chain-of-thought may be the next-gen reasoning format for VLMs, replacing verbal-only reasoning
- Key insight: visual and even embodied reasoning don't require explicit pixel reconstruction—generating latents/embeddings suffices, and pretrained vision encoders (e.g., Qwen2.5-VL's) already provide good representations
Related event: UCSD's WM-VLM Boosts VLM Spatial Reasoning via Internal World Model(2 posts)→
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